index.search
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
index.search is searching the index with query embedding.
Mostly:rdf:type(8), returns(7), uses(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (20)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
callsCalls(4)
- Code Snippet
ex:code-snippet - Search Similar Vectors
ex:search-similar-vectors - Search Vectors
ex:search_vectors - Search Vectors Function
ex:search-vectors-function
precedesPrecedes(3)
- Document Addition
ex:document-addition - Index Addition
ex:index-addition - Vector Normalization
ex:vector-normalization
callsMethodCalls Method(2)
- Faiss Code Block
ex:faiss-code-block - Index Search Call
ex:index-search-call
callsIndexSearchCalls Index Search(1)
- Retrieve Documents
ex:retrieve_documents
containsContains(1)
- Protected Operation Block
ex:protected-operation-block
containsStatementContains Statement(1)
- Python Code Example
ex:python-code-example
describesDescribes(1)
- Search Step
ex:search-step
enclosesEncloses(1)
- Try Except Structure
ex:try-except-structure
isTargetOfIs Target of(1)
- Search Index
ex:search_index
isUsedByIs Used by(1)
- Search Body
ex:search-body
performsPerforms(1)
- Refine Function
ex:refine-function
searchedBySearched by(1)
- Index Object
ex:index-object
storesOutputOfStores Output of(1)
- Results Assignment
ex:results-assignment
usesMethodUses Method(1)
- Faiss Search
faiss-search
Other facts (37)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Information Retrieval Operation | [1] |
| Rdf:type | Code Operation | [2] |
| Rdf:type | Method | [4] |
| Rdf:type | Index Operation | [6] |
| Rdf:type | Method | [7] |
| Rdf:type | Search Method | [8] |
| Rdf:type | Faiss Search Operation | [10] |
| Rdf:type | Faiss Search Method | [11] |
| Returns | Distances | [5] |
| Returns | Indices | [5] |
| Returns | Search Results | [6] |
| Returns | Distances | [7] |
| Returns | Indices | [7] |
| Returns | Search Results | [10] |
| Returns | Distances and Indices | [11] |
| Uses | Cosine Similarity Metric | [1] |
| Uses | Query Embedding Parameter | [6] |
| Uses | K Parameter | [6] |
| Has Parameter | Search Body | [2] |
| Has Parameter | K Value | [5] |
| Has Arguments | Query Vector | [3] |
| Has Arguments | K | [3] |
| Parameter | Query Embedding | [7] |
| Parameter | K Parameter | [7] |
| Computes | Distance Metrics | [9] |
| Computes | Nearest Indices | [9] |
| Targets | Search Index | [2] |
| Precedes | Results Assignment | [2] |
| Calls Method | Search Method | [2] |
| Operates on | Hnsw Index | [4] |
| Accepts Argument | Query Vector Reshaped | [4] |
| Is Called by | Search Similar Vectors | [4] |
| Description | searching the index with query embedding | [6] |
| Follows | Index Addition | [6] |
| Reads | Faiss Index Object | [6] |
| Called on | Index Object | [12] |
| Passes K | 5 | [12] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (12)
ctx:claims/beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3a- full textbeam-chunktext/plain1 KB
doc:beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3aShow excerpt
6. **Build Index**: Use Faiss to build an index of the document vectors. 7. **Search and Retrieve**: Encode the query into a vector, normalize it, and search the index to find the most similar documents based on cosine similarity. ### Conc…
ctx:claims/beam/837f35de-3ee9-47a5-a635-98cff17d7ea2- full textbeam-chunktext/plain836 B
doc:beam/837f35de-3ee9-47a5-a635-98cff17d7ea2Show excerpt
[Turn 1298] User: I'm trying to build a system to support 3 distinct search modules, each handling 20,000 queries daily with under 250ms latency. I'm considering using Elasticsearch 8.7.0 for sparse retrieval, but I'm not sure if it's the r…
ctx:claims/beam/3b1e0a95-da47-45cb-81f4-b8a0f4b99a3c- full textbeam-chunktext/plain1 KB
doc:beam/3b1e0a95-da47-45cb-81f4-b8a0f4b99a3cShow excerpt
import numpy as np import faiss # Assuming I have a dataset of vectors vectors = np.random.rand(1000, 128).astype('float32') # Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Build an index using FAISS index = f…
ctx:claims/beam/9080e26c-2d73-4ed8-801c-d290a10ff5c0ctx:claims/beam/c4c1ef0d-4b8c-4ad5-8952-807c68abe498- full textbeam-chunktext/plain1 KB
doc:beam/c4c1ef0d-4b8c-4ad5-8952-807c68abe498Show excerpt
By following these strategies and implementing the backoff and retry mechanism, you should be able to prevent `PartitionFullException` and ensure that your streaming uploads complete successfully. Let me know if you need further assistance …
ctx:claims/beam/c93f21b2-5d63-4700-acd2-ac16decca67bctx:claims/beam/a8f9767f-e515-4c18-876d-5a6237129dbe- full textbeam-chunktext/plain1 KB
doc:beam/a8f9767f-e515-4c18-876d-5a6237129dbeShow excerpt
query_embedding = np.random.rand(1, 512).astype('float32') # Search the index distances, indices = index.search(query_embedding, k=10) print(distances) print(indices) ``` ->-> 4,22 [Turn 4869] Assistant: Certainly! FAISS is a powerful li…
ctx:claims/beam/9d96f8cb-54e9-48bd-a699-50a1796601b9ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b- full textbeam-chunktext/plain1 KB
doc:beam/9170f193-72c4-43d3-9c09-87f869d91b8bShow excerpt
index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…
ctx:claims/beam/cd9b13af-512f-4087-b34b-2124116b3091- full textbeam-chunktext/plain1 KB
doc:beam/cd9b13af-512f-4087-b34b-2124116b3091Show excerpt
# Define the vector search function. def search_vectors(tokens): # Create a FAISS query. query = np.array([vector for vector in tokens]).astype('float32') # Search for similar vectors. distances, indices = index.search(quer…
ctx:claims/beam/c6f95027-c797-4e8f-881b-eab184fc2873- full textbeam-chunktext/plain1 KB
doc:beam/c6f95027-c797-4e8f-881b-eab184fc2873Show excerpt
from flask import Flask, request, jsonify import redis import spacy import faiss import numpy as np # Initialize the Flask app app = Flask(__name__) # Load the SpaCy model try: nlp = spacy.load("en_core_web_sm") except OSError as e: …
ctx:claims/beam/83decc01-f770-4428-852b-466b97d6139c- full textbeam-chunktext/plain1 KB
doc:beam/83decc01-f770-4428-852b-466b97d6139cShow excerpt
expanded_query = query for lang in languages: if lang != 'en': # Use translation API or model to expand query # For simplicity, we assume a translation function `translate` translated_quer…
See also
- Cosine Similarity Metric
- Information Retrieval Operation
- Code Operation
- Search Index
- Search Body
- Results Assignment
- Search Method
- Query Vector
- K
- Method
- Hnsw Index
- Query Vector Reshaped
- Search Similar Vectors
- K Value
- Distances
- Indices
- Index Operation
- Search Results
- Index Addition
- Faiss Index Object
- Query Embedding Parameter
- K Parameter
- Query Embedding
- Search Method
- Distance Metrics
- Nearest Indices
- Faiss Search Operation
- Faiss Search Method
- Distances and Indices
- Index Object
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